Multimodal Alzheimer’s Disease Classification From Heterogeneous Datasets Using Multihead Cross‐Attention CNN–BiLSTM With BAM

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that requires reliable computational approaches to interpret complex and heterogeneous medical data. Many existing diagnostic methods rely on single‐modality analysis or multimodal approaches that require strictly paired datasets, which limits their applicability when correspondences across modalities are unavailable. To address this limitation, this study proposes a multimodal deep learning framework for binary AD classification that integrates MRI, PET, clinical tabular data, and speech‐derived audio features. A label‐consistent feature‐level fusion strategy enables learning from heterogeneous datasets while maintaining class consistency across modalities. Modality‐specific preprocessing is applied to preserve domain characteristics and improve feature quality across data sources. High‐level representations are extracted from imaging data using ResNet‐50, clinical variables are projected into a shared latent representation through fully connected layers, and speech recordings are converted into discriminative audio feature representations. The multimodal features are fused using a multihead cross‐attention mechanism to capture complementary intermodal relationships and produce a shared multimodal representation. The fused features are then processed using a CNN–BiLSTM architecture with a bottleneck attention module (BAM) for refined feature learning. Experimental evaluation achieves 92.57% accuracy, 93.17% precision, 91.88% recall, 92.47% F1‐score, and 0.9732 AUC, demonstrating effective multimodal AD classification.